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A NON-PARAMETRIC BAYESIAN APPROACH FOR PREDICTING RNA SECONDARY STRUCTURES

机译:预测RNA二级结构的非参数贝叶斯方法

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Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.
机译:由于许多功能性RNA形成与其功能相关的稳定二级结构,因此RNA二级结构预测是生物信息学中的关键问题。我们提出了一种基于非参数贝叶斯方法生成RNA二级结构的新颖模型,该方法称为随机上下文无关文法(HDP-SCFG)的分层Dirichlet过程。在此,非参数表示不必固定某些元参数,例如非终端符号的数量和生产规则。而是推断它们的分布,以便(在贝叶斯意义上)适应提供的训练序列。我们的RNA二级结构预测结果表明,HDP-SCFG比基于MFE的模型和其他生成模型更准确。

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